Atmospheric Radiation Measurement Climate Research Facility US Department of Energy

varanal > Constrained Variational AnalysisVAP Type(s) > Baseline • Evaluation • External • Guest

The large-scale forcing data is derived based on the constrained variational analysis approach (Zhang and Lin, 1997; Zhang et al. 2001), which calculates the large-scale vertical velocity and advective tendencies from sounding measurements of winds, temperature, and water vapor mixing ratio over a network of a small number of stations.

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Major updates in the VARANAL v2 include: 1) the method used to develop multi-year continuous forcing data, 2) the incorporation of Eddy Correlation Flux Measurement System (ECOR) turbulent fluxes into the analysis, and 3) improvements to the workflow to increase efficiency.

Currently, two major VARANAL data sets are archived by ARM: 1) Radiosonde- or numerical weather prediction (NWP)-based forcing data for short-term Intensive Operational Periods (IOPs) at different ARM fixed or mobile sites. Most of the IOP forcing is at 3-hr and 25-mb resolutions. 2) Multi-year continuous forcing data at the ARM observatories. The continuous forcing is at 1-hr and 25-mb resolutions.

This VAP has been used to drive Single-Column Models (SCMs), Cloud-Resolving Models (CRMs) and Large-Eddy Simulation Models (LESs) for different cloud and convective systems. Results from these model simulations are then used to improve cloud parameterizations in Global Climate Models (GCMs). It can also be applied to evaluate model results, as it includes diagnostic fields such as diabatic heating profiles, cloud fields, surface measurements, and large-scale conditions. Additionally, VARANAL is one of the critical datasets required for the ongoing routine LES ARM Symbiotic Simulation and Observation  (LASSO).

The derivations of the VARANAL from field measurements are subject to uncertainties that can directly impact the simulated cloud and radiation fields by SCM/CRM/LES. These uncertainties originate from two sources: 1) the instrument and measurement errors, and 2) the scale aliasing or sampling biases errors. Both error types depend on scales because horizontal derivatives are involved in the calculation of the horizontal fluxes. Ensemble forcing data by perturbing potential uncertainties in the constraints can help address this type of uncertainty in the forcing data. Please refer to the technical report for more information.


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